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Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

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Hysteresis and return-point memory in colloidal artificial spin ice systems.

A Libál¹, C Reichhardt, C J Olson Reichhardt

¹Faculty of Mathematics and Computer Science, Babes-Bolyai University, RO-400591 Cluj-Napoca, Romania.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 26, 2012

Summary

Computer simulations reveal how artificial spin ice systems lose and regain memory. Kagome ice recovers memory faster than square ice due to defect and grain boundary dynamics, with disorder enhancing memory retention.

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Area of Science:

Condensed Matter Physics
Computational Materials Science

Background:

Artificial spin ice (ASI) systems exhibit complex magnetic behaviors.
Hysteresis loops and return-point memory are key characteristics of ASI dynamics.

Purpose of the Study:

To investigate the mechanisms behind hysteresis and return-point memory in square and kagome ASI.
To compare the memory recovery dynamics in different ASI geometries.

Main Methods:

Utilizing computer simulations to model ASI systems.
Cycling an applied bias force to induce hysteresis.
Comparing microscopic effective spin configurations during hysteresis cycles.

Main Results:

Return-point memory loss is attributed to defect motion in kagome ice and grain boundary motion in square ice.
Kagome ice exhibits rapid return-point memory recovery in successive cycles.
Square ice shows gradual memory recovery due to extended grain boundaries.
Increased quenched disorder enhances return-point memory by trapping defects more effectively.

Conclusions:

Defect and grain boundary dynamics critically influence memory effects in ASI.
Kagome and square ASI display distinct memory recovery behaviors.
Quenched disorder can be leveraged to improve memory retention in ASI systems.